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Record W2987965887 · doi:10.1002/ppul.24555

How to use noninvasive positive airway pressure device data reports to guide clinical care

2019· review· en· W2987965887 on OpenAlexaff
Lucy Perrem, Kevan Mehta, Faiza Syed, Adele Baker, Reshma Amin

Bibliographic record

VenuePediatric Pulmonology · 2019
Typereview
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsSickKids FoundationUniversity of Toronto
Fundersnot available
KeywordsMedicineIntensive care medicineClinical PracticeMEDLINEPositive airway pressureAirwayMedical physicsPediatricsSurgeryPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

There has been a significant increase in the past few decades in the number of children receiving noninvasive positive airway pressure (PAP) therapy at home. At present, PAP therapy can be successfully used in children of all ages, for a variety of indications. Data acquired from PAP devices is clinically useful, providing objective information regarding adherence, leak, and efficacy of PAP therapy. However, guidelines outlining a standardized approach to interpretation of PAP device data in pediatrics is currently lacking. Given the rapidly expanding use of PAP therapy in pediatric practice, we aim to provide an overview of the interpretation of data reports, otherwise called "data downloads," from PAP devices and illustrate how they can be used to guide clinical care.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.954
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.154
GPT teacher head0.445
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations28
Published2019
Admission routes1
Has abstractyes

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